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Machine Learning
Last updated: Feb 8, 2022

Machine Learning

Machine Learning is the ability of machines to learn. Machine Learning is a branch of Artificial Intelligence. The various applications of Machine Learning are used in the Information Technology(IT), medical and education industries. For instance, in modern times, companies widely use Machine Learning for Data Analytics to get more customer engagement. Machine Learning works by learning patterns from a training dataset and applying it to an unseen real-world dataset. The five steps in a Machine Learning pipeline are defining the problem, building the dataset, training the model, evaluating the model, and finally using the model to generate predictions.
Difference Between Machine Learning and Deep Learning MEDIUM
Explore differences between Machine Learning and Deep Learning, their meanings, and types. Dive into a detailed comparison of these AI technologies.
Author
2 upvotes
Gradient Boosting Machine EASY
This article discusses the theoretical knowledge about gradient boosting.
Author Siddhant_007
1 upvote
Regression Model in Machine Learning
A regression model is basically a representation of a set of independent quantity on a unit dependent quantity, that dependent quantity in machine lea...
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0 upvotes
Stacking in Machine Learning MEDIUM
Stacking, also known as stacked generalization, is an ensemble learning technique in machine learning that combines multiple different models to improve overall performance
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0 upvotes
Data Analytics vs Machine Learning MEDIUM
In this article, we will explore the key differences between Data Analytics and Machine Learning, their applications, and how they impact decision-making.
Author Rahul Singh
0 upvotes
Cost Function in Machine Learning MEDIUM
This article will explain what a cost function is, why it's essential, and the different types used for various machine learning tasks, including regression and classification.
Author Sanjana kumari
0 upvotes
Deep learning with Tensorflow and Keras MEDIUM
Deep Learning is nowadays on the boom because of the frameworks like Tensorflow and Keras. It has become easy to make Machine Learning model without a...
Author
0 upvotes
Boosting with AdaBoost
Boosting with AdaBoost is a short form for “Adaptive Boosting” which is the first practical boosting algorithm proposed by Freund and Schapire in 1996...
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0 upvotes
Linear Regression in Machine Learning MEDIUM
In this blog, We'll see what a Regression problem is, and then we'll study the working of the Linear Regression Algorithm and its implementation
Author Akash
0 upvotes
Top 13 Machine Learning projects in R MEDIUM
Machine Learning and Artificial Intelligence have reached a critical tipping point & will increasingly augment and extend virtually every technology-e...
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0 upvotes
Feature Engineering for Machine Learning MEDIUM
In this blog, we will learn about Feature Engineering for Machine Learning. We will learn about its tools and later look at the libraries used in machine learning.
Author Gaurav Gandhi
0 upvotes
Top 20 Datasets in Machine Learning
Dataset is an integral part of Machine Learning applications. It can be available in different formats like .txt, .csv and many more. The article prov...
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0 upvotes
Statistics for Machine Learning MEDIUM
In this article, we will learn about key statistics concepts for Machine Learning, including probability, hypothesis testing, regression, and distributions.
Author Rahul Singh
0 upvotes
5 Best AI and ML startup hirings in 2024 MEDIUM
we’re witnessing the many, many, marvels of Artificial Intelligence that are ruling our lives. From the IT sector to banking and fintech, from healthc...
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0 upvotes
Bayesian Learning in Machine Learning MEDIUM
In this blog, we will learn about Bayesian Learning in Machine Learning. We will learn Bayes' Theorem, terms related to, and applications of Bayes' Theorem.
Author Riya Singh
0 upvotes
Decision Tree Regression MEDIUM
In this article, we will learn about Decision Tree Regression, how it works, its advantages, and how it is used for predicting continuous values in machine learning.
Author Gaurav Gandhi
0 upvotes
How to create Anime Faces using GANs in PyTorch?
AI-based filtering, face-changing and photo-editing software have taken the internet by storm. People today spend endless hours trying to find the per...
Author
0 upvotes
Issues in Machine Learning EASY
In this blog, we will learn about Issues in Machine Learning. Understanding these issues can help you create better ML models and use them more effectively.
Author Rinki Deka
0 upvotes
How to Install CV2 in Python EASY
In this article, we will learn how to install OpenCV (cv2) in Python, which is a popular programming language for computer vision applications.
Author Ravi Khorwal
0 upvotes
Difference Between Deep Learning vs Machine Learning vs Artificial Intelligence MEDIUM
In this article, we will learn the key differences between Deep Learning, Machine Learning, and Artificial Intelligence, their applications, and real-world use cases.
Author Gaurav Gandhi
0 upvotes
Top Machine Learning experts to follow in 2024
The technological landscape is rapidly changing. Today, we have concepts, ideas, and realities that were a thing of sci-fi creators/ experts’ imaginat...
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0 upvotes
What is Machine Language? MEDIUM
In this blog, we will learn about What is machine language. We will understand its core concepts, its usage, and much more for better understanding.
Author Rahul Singh
0 upvotes
Entropy in Machine Learning EASY
In this blog, we will learn about Entropy in Machine Learning. We will learn about how entropy is calculated and later in the end will look at the example for entropy.
Author Rahul Singh
0 upvotes
Anomaly Detection in Machine Learning MEDIUM
In this article, we'll explore anomaly detection, its types, techniques, key features, challenges, and answer some frequently asked questions.
Author Rahul Singh
0 upvotes
Top 10 Libraries for Data Visualisation MEDIUM
Data Visualisation is a tool used by Data Scientists to convey important information represented by it to other people who are not experts in the doma...
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0 upvotes
Epoch in Machine Learning MEDIUM
In this blog, we will learn about Epoch in Machine Learning. We will understand each concept in detail and later look at the advantages and disadvantages.
Author Sanjana kumari
0 upvotes
Dempster Shafer Theory EASY
In this blog, we will learn about Dempster Shafer's Theory. We will understand its core concepts, its usage, and much more for better understanding.
Author Riya Singh
0 upvotes
Career Prospects After a Course on Machine Learning MEDIUM
Machine Learning is emerging as one of the most sought-after career prospects today. It is likely to create 2.3 Mn. ML-related jobs by 2023 — accordin...
Author
0 upvotes
Machine Learning Applications EASY
In this blog, we will explore Machine Learning Applications. We will learn about how machine learning helps in these applications and will write code and much more for better understanding.
Author Sanjana kumari
0 upvotes
Regularization in Machine Learning MEDIUM
In this blog, we will learn about Regularization. We will understand its core concepts, its usage, and much more for better understanding.
Author Rahul Singh
0 upvotes
Top 10 Apps designed using Machine Learning
According to Gartner, a research and advisory company, there will be 2.3 million jobs in the field of Artificial Intelligence and Machine Learning by...
Author
0 upvotes
Unification in Artificial Intelligence MEDIUM
Unification in AI is the process of finding a substitution that makes different logical expressions identical, enabling reasoning and problem-solving in AI systems.
Author Sanjana kumari
0 upvotes
Types of Environments in AI EASY
In this blog, we will learn about different Types of Environments in AI in complete depth along with examples for each.
Author Sinki Kumari
0 upvotes
5 Best Machine Learning projects
The heap of data that is created each day by every single person is only going to increase with time. This is precisely what facilitates the need for...
Author
0 upvotes
What Are Data Loss Prevention Techniques? MEDIUM
As defined by Cisco, “Data loss prevention, or DLP, is a set of technologies, products, and techniques that are designed to stop sensitive information...
Author Anuradha Dixit
0 upvotes
Artificial Intelligence and Machine Learning EASY
In this blog, we will learn about Artificial Intelligence and Machine Learning. We will understand each concept in detail and later look at the difference for better understanding.
Author Sanjana kumari
0 upvotes
History of AI EASY
In this blog, we will learn about History of AI.
Author Rahul Singh
0 upvotes
Generative AI Models EASY
In this article, we will study generative AI models. First, we will cover what generative AI is, and then we will move to generative AI models.
Author Nidhi Kumari
1 upvote
Handling Outliers In Data Science
This article will discuss Handling Outliers In Data Science, how to identify them, the causes of outliers, strategies to handle those outliers, and when to use and remove them.
Author Arya27
1 upvote
Goal Stack Planning in AI EASY
In this article, we will learn about the concept of goal stack planning, its implementation in Java, goal decomposition, & dynamic goal selection.
Author Rinki Deka
0 upvotes
Contour plots EASY
In this blog, we will learn about Contour plots. We will start with implementing and later in the blog will go through applications.
Author Sinki Kumari
0 upvotes
The 8 Puzzle Problem in AI MEDIUM
The 8 Puzzle problem in AI involves sliding tiles on a 3x3 grid to reach a goal state, testing algorithms like A* and BFS for efficient solution finding.
Author Sinki Kumari
0 upvotes
Differences Between Artificial Intelligence and Human Intelligence EASY
Differences Between Artificial Intelligence and Human Intelligence use its brain & memory to gather data where as AI is dependent on the data provided by the humans Intelligence.
Author yuvatimankar
0 upvotes
Utility Theory In Artificial Intelligence EASY
In this article, we'll discuss utility theory in artificial intelligence. We will start with the utility function. We’ll also cover the formula for utility functions.
Author Nidhi Kumari
1 upvote
Simulated Annealing in AI EASY
In this blog, we will learn about Simulated Annealing in AI. We will understand its core concepts,features, and much more for better understanding.
Author Ravi Khorwal
0 upvotes
Chatbots in AI EASY
This blog will discuss chatbots in AI along with their working, some applications and limitations along with some frequently asked questions.
Author Aayush Sharma
0 upvotes
Informed Search Algorithms in Artificial Intelligence EASY
In this article, we'll learn about different types of informed search algorithms, how they work, their pros & cons, & see some examples.
Author Sanjana kumari
0 upvotes
Loan Default Prediction EASY
In this blog, we will learn about Loan Default Prediction. We will also explore the step-step guide of the process.
Author Lekhika
0 upvotes
How to Use ChatGPT By OpenAI
In this article, we will learn how to use ChatGPT by OpenAI in detail. we also include some best practices for using ChatGPT.
Author Kanak Rana
0 upvotes
Inference Engine in AI EASY
Read about Inference engine in AI with examples, types, advantages, and disadvantages. Also covers Forward Chaining and Backward Chaining in AI and components.
Author yuvatimankar
1 upvote
Probabilistic Reasoning in Artificial Intelligence MEDIUM
This blog discusses the topic of Probabilistic Reasoning in Artificial Intelligence along with its definition, need, working, types and applications.
Author Riya Singh
2 upvotes
Local Search Algorithm in Artificial Intelligence EASY
This blog will discuss the Local Search Algorithm with its definition, working, types, pros and cons.
Author Sanjana kumari
1 upvote
Wumpus World in AI EASY
This article discusses wumpus world in ai with problem statement. Explore the PEAS description and properties of the wumpus world.
Author DEVENDRA KUMAR
2 upvotes
Difference between Bagging and Boosting
This blog gives a brief description of bagging and boosting along with the implementation steps of both and the difference between bagging and boosting.
Author Avni Gupta
2 upvotes
Data Science Applications
In this article, we will learn what data science is and the different areas in which data science is applicable.
Author Nikhil Joshi
0 upvotes

Introduction

Introduction to every Machine Learning concept and application, including real-world examples, code implementation, and mathematical theory. Learn about the fundamental differences, types, and implementation.
Data Science VS Artificial Intelligence VS Machine Learning VS Deep Learning
I hope you are doing well. In this blog, we will learn the aspects of data and the process which converts it into a structured format. We will also compare data science, Artificial Intelligence, Machine Learning, and Deep Learning.
Author Rajkeshav
1 upvote
What is Machine Learning? MEDIUM
This article will give you a complete introduction to machine learning, its types, models, features, tools, advantages, disadvantages, limitations, applications, and future scope.
Author Tashmit
11 upvotes
How does Machine Learning work?
In this blog, we’ll learn about how machine learning works, its types, applications, and its importance.
Author Anant Dhakad
3 upvotes
Types of Machine learning EASY
Machine learning is a branch of AI that enables systems to learn from data and improve performance. Types of machine learning include supervised, unsupervised, Semi-Supervised, and reinforcement learning.
Author Pratyksh_7855
3 upvotes
Top Applications of Artificial Intelligence (AI) in 2023 MEDIUM
In this article, we will discuss about Artificial Intelligence (AI) and also check out the top applications of Artificial Intelligence in 2023 in detail.
Author Md Yawar
0 upvotes
Heuristic Search Techniques in Artificial Intelligence EASY
In this blog, we will learn about Heuristic Search Techniques in Artificial Intelligence. We will understand its core concepts, its usage, types, and much more for better understanding.
Author Gaurav Gandhi
0 upvotes
Subsets Of Artificial Intelligence EASY
This article explores the various subsets of Artificial Intelligence, shedding light on their diversity and applications.
Author Arya27
0 upvotes
Bagging Machine Learning MEDIUM
In this blog, we will learn about Bagging Machine Learning. We will understand its core concepts, its usage, and much more for better understanding.
Author Pallavi singh
0 upvotes
Hill Climbing Algorithm in AI EASY
In this blog, we will learn about Hill Climbing Algorithm. We will understand its core concepts, its usage, and much more for better understanding.
Author Rahul Singh
0 upvotes
Advantages and Disadvantages of Artificial Intelligence EASY
Explore the pros and cons of Artificial Intelligence and learn how AI empowers machines to make decisions and predictions efficiently.
Author Md Yawar
1 upvote
Artificial Intelligence Salary in India
This article discusses different careers in AI, the skills they need, and their expected salaries.
Author AkshatAggarwal
1 upvote
Alpha Beta Pruning MEDIUM
Alpha beta pruning in Artificial Intelligence is a way of improving the minimax algorithm. It is a famous backtracking algorithm used in decision-making.
Author Md Yawar
2 upvotes
State Space Search in Artificial Intelligence MEDIUM
State space search is a strategy used in Artificial Intelligence to find a path to a goal when there are many possibilities to consider.
Author Pallavi singh
0 upvotes
Hidden Markov Model in Machine Learning EASY
In this blog, we will learn about Hidden Markov Model in Machine Learning. We will understand its core concepts, its usage, types, and much more for better understanding.
Author Rahul Singh
0 upvotes
First-Order Logic in Artificial Intelligence EASY
In this article, we will discuss First-Order Logic in Artificial Intelligence, Notations, Quantifiers and Resolution in Artificial Intelligence.
Author Pallavi singh
2 upvotes
Asymmetric Key Cryptography MEDIUM
In this blog, we will learn about Asymmetric Key Cryptography. We will learn about its characteristics, features, and much more for better understanding.
Author Gaurav Gandhi
0 upvotes
Predicate Logic In AI MEDIUM
Predicate Logic or First-Order Logic (FOL) is used to represent complex expressions in easier forms using predicates, variables, and quantifiers.
Author Rinki Deka
2 upvotes
Philosophy of AI
In this blog, we will discuss the philosophy of AI. We will also explore the differences between strong and weak AI.
Author mishrashriCodes
1 upvote
Expert System in Artificial Intelligence (AI) MEDIUM
In this blog, we will discuss the expert systems developed with the help of artificial intelligence in detail along with their advantages and disadvantages.
Author Rinki Deka
1 upvote
Underfitting and Overfitting in ML EASY
The objective of this blog is to understand what is underfitting and overfitting in ML.
Author Toohina Barua
2 upvotes
Top 20 A.I Artificial Intelligence Project Ideas in 2024 EASY
This article discuss the Top 20 A.I Artificial Intelligence projects in 2024 for beginners, intermediate and advanced.
Author Nikunj Goel
1 upvote
Production System in AI EASY
A production system in AI refers to a computer-based system that is designed to automate and manage the production or execution of some process.
Author Pallavi singh
0 upvotes
Artificial Intelligence Questions MEDIUM
This article compiles the most asked artificial intelligence interview questions. Dive in and elevate your AI knowledge to ace your AI interviews.
Author Md Yawar
0 upvotes
Future of Artificial Intelligence EASY
The bright future of artificial intelligence has the potential to drive advancements in autonomous vehicles across multiple industries.
Author Vidhu Chaudhary
0 upvotes
Agent in Artificial Intelligence EASY
Explore agent in artificial intelligence: how they work, structure, functions, and challenges. Learn about types of agents in AI, PEAS representation, and examples.
Author Vidhu Chaudhary
0 upvotes
Adversarial Search in Artificial Intelligence MEDIUM
In this article, we'll look at Adversarial Search in Artificial Intelligence, different game scenarios using adversarial search, and its need in artificial intelligence.
Author Gaurav Gandhi
1 upvote
Forward Chaining and backward chaining in AI
This blog discusses the concept of forward and backward chaining in AI with their examples in detail. Explore Forward Chaining and backward chaining in AI with advantages and disadvantages.
Author Ravi Khorwal
1 upvote
Non-Linear Planning in AI
The article will discuss non-linear planning in AI, explore its components and heuristics, and discover how it revolutionizes decision-making and flexibility.
Author Harshita Rajput
1 upvote
12 Most Used Machine Learning Algorithms in Python MEDIUM
Discover the 12 most used machine learning algorithms in Python. Learn key ML techniques with examples for supervised, unsupervised & reinforcement learning.
Author Gunjan Batra
0 upvotes
Data Science vs Machine Learning EASY
In this article, we will learn the difference between Data Science and Machine Learning.
Author Juhi Sinha
0 upvotes

Tools for Machine Learning

Important tools for improving your performance while developing Machine Learning models and algorithms. Discover one of the most efficient IDEs, Data Science packages, and much more.
Ultimate Package - Anaconda
This article will look into Anaconda, what makes the conda environment peculiar, and some of the basic commands used in the conda environment.
Author Mayank Goyal
1 upvote
What is AutoML in Machine Learning?
This article will discuss autoML, its applications, and its features. Information about the organization that supports autoML.
Author soham Medewar
0 upvotes
Applications of AutoML
In this blog, we’ll understand what AutoML is and AutoML’s advantages over the custom Machine Learning architecture; we’ll also see some of the AutoML Libraries.
Author Chaturvedi
0 upvotes
Binning in Data Mining MEDIUM
Discover the concept of binning in data mining. Learn how data is grouped into bins to simplify analysis, reduce noise, and enhance the accuracy of models.
Author Vidhi Sareen
1 upvote
'ValueError: Trailing data' error Resolution EASY
In this article, we will discuss why does the 'ValueError: Trailing data' occurs and how to prevent it.
Author soham Medewar
0 upvotes

Statistics & Probability

Statistics and probability are the best tools for solving problems, whether they are real-world problems or optimising machine learning models. Making sense of data and extracting meaningful information from it using statistical techniques, formulas, and variable moderation is an important first step in working on machine learning or deep learning systems.
Descriptive Statistics MEDIUM
This article will discuss descriptive statistics and types with clear information about descriptive statistics.
Author Arya27
1 upvote
Variance and standard deviation EASY
This article begins with an introduction to variance and standard deviation, how to calculate them, and their implementation in python.
Author Pradipta Choudhury
0 upvotes
Skewness and Kurtosis
This article will study two statistical concepts,i.e., Skewness and kurtosis, and their applications.
Author Mayank Goyal
2 upvotes
Combinatorics for ML MEDIUM
This blog talks about combinatorics method and its application in Machine learning.
Author Mohammad Yawar
1 upvote
Categorical Data Categorical data is a subtype of data that is commonly discovered while extracting and working with datasets. The categorical data is made up of various divided categories that can include both numbers and characters.
Numerical Data Another type of data that is commonly found is numerical data, which consists primarily of numbers. It focuses on quantitative data collection. It is very simple to perform various mathematical operations on numerical data and to easily analyse the data.
Probability Probability is another important mathematical aspect that is usually required for data analysis, algorithm optimization, and model optimization. This domain is useful for dealing with data that has a lot of variables and performing operations on it.

KickStart to Machine Learning

Gear to know about libraries and packages that are essential to perform Machine learning techniques and build models. Master code implementation of Numpy and Pandas with Linear Algebra including matrices, vectors, and statistics.
13 Books to master Machine Learning MEDIUM
According to Gartner, a research and advisory company, there will be 2.3 million jobs in the field of Artificial Intelligence and Machine Learning by...
Author
0 upvotes
5 On-Going Trends in the Field of Deep Learning
Deep learning has been popular for quite some time now. It has brought several benefits to many businesses since deep learning has boosted the effecti...
Author
0 upvotes
Numpy and Linear Algebra Learn about the Numpy functions needed to get started with Machine Learning, as well as code snippets and mathematical concepts related to Linear Algebra.
Pandas and Datasets Pandas is a very important and interesting Python library for data handling and dataset operations. Learn about its methods, applications, and implementations that will help you clean up your data
SciPy Library SciPy is a Python library for performing scientific and mathematical calculations. It covers everything from basic linear algebra to advanced calculus, differential equations, and engineering problems. A must-read for Python and science enthusiasts.
scikit-learn Library The scikit-learn package is a fascinating and valuable library for machine learning operations. It includes a wide range of supervised and unsupervised learning algorithms, from classification to regression. Methods and functions that are simple to invoke.

Data Analysis

No Machine Learning model can function without proper analysis, preprocessing, and knowledge of the dataset. Data analysis is one of the most important techniques to perform before performing Machine Learning techniques because it is responsible for the model's better predictions and accuracy.
Introduction to Data Analysis
In this blog, we will learn about the importance and applications of Data Analysis, and then we will learn about tools used for Data Analysis with a primary focus on python.
Author Ritik Arora
1 upvote
Neighbourhood Aggregation in GraphX MEDIUM
In this article, we will discuss about neighbourhood aggregation in GraphX, collecting neighbours and aggregating messages.
Author shruti_f97c
0 upvotes
Learning about Tableau Interface
This article discusses the Tableau Interface and its main components for efficiently creating visualisations and data analysis.
Author AkshatAggarwal
1 upvote
Introduction to Tableau EASY
This article covers the concept of tableau, its features, uses, advantages, and disadvantages.
Author 3053_Jatin
0 upvotes
Importing Data to Tableau EASY
This article explains how to import data in tableau and importance of data in tableau.
Author abhay_rathi2435
0 upvotes
Making Bullet Chart in Tableau EASY
This article covers a step-by-step tutorial about making a bullet chart in Tableau.
Author abhay_rathi2435
0 upvotes
Data Types in Tableau
This article covers all the different types of data types and how can we change the data type of fields in Tableau.
Author Gaurav Chandakant Kolte
0 upvotes
Parameters in Tableau EASY
In this blog, we will discuss the Parameters in Tableau. We will discuss their benefits and learn how to create and use these parameters in Tableau.
Author kanishk11
0 upvotes
Dimensions and Measures in Tableau EASY
This article covers the concept of Dimensions and Measures in Tableau, their examples, respective uses, and the process of converting a Dimension to a Measure.
Author Priyanshu Grover
0 upvotes
Data Cleaning with Data Interpreter MEDIUM
The article covers the concepts of data cleaning with Data interpreter, its usage, and its implementation along with some frequently asked questions.
Author Abhiyodaya Pandey
0 upvotes
Groups and Sets in Tableau MEDIUM
The article covers the concepts of groups and sets in Tableau, their differences, and implementation, along with some frequently asked questions.
Author Abhiyodaya Pandey
0 upvotes
Introduction to Dashboard in Tableau MEDIUM
The article covers the concepts of introduction to Dashboard in Tableau, its features, and its implementation along with some frequently asked questions.
Author Abhiyodaya Pandey
0 upvotes
Data Analysis Process EASY
In this blog, we will learn about Data Analysis Process. We will understand its core concepts,steps, and much more for better understanding.
Author Sanjana kumari
1 upvote
Introduction to Tooltips in Tableau
This article covers the concept of Tooltips in Tableau, its features, implementation of its methods, applications, and some frequently asked questions.
Author Priyanshu Grover
0 upvotes
What are Aliases in Tableau? MEDIUM
This article discusses tableau aliases, including their features, construction, data blending capabilities along with some frequently asked questions.
Author Aayush Sharma
0 upvotes
Relationships vs Joins
In this blog, we are going to see the difference between Relationships and Joins and their pros and cons.
Author DEVENDRA KUMAR
0 upvotes
Relationships in Tableau
This blog will discuss the new feature added to Tableau called relationships, along with its features and operations.
Author Sohail Ali
1 upvote
Making Waterfall Chart in Tableau
In this article we will be studying about making waterfall charts in Tableau, its features, how to create waterfall charts, its uses and step-by-step procedure to making waterfall chart in Tableau.
Author Nitika
0 upvotes
Making a Dual Axis Chart in Tableau
Dual Axis charts in Tableau are powerful visualizations that allow users to combine two different measures or dimensions on separate axes within a single chart.
Author Gaurav Chandakant Kolte
0 upvotes
Sorting Data in Tableau EASY
This article covers the concept of Sorting Data in Tableau, its features, various methods to Sort the data, and Sorting using Parameters.
Author Priyanshu Grover
0 upvotes
Introduction to Extract Filter EASY
This article covers a brief Introduction to Extract filters in Tableau. It covers what an extract filter is, its features and its implementation.
Author Shiva
0 upvotes
Exploratory Data Analysis In Python MEDIUM
In this article, we'll discuss the basics of EDA using Python. We'll cover data preprocessing, univariate analysis, bivariate analysis, & multivariate analysis.
Author Rahul Singh
0 upvotes
Implementing Tables in Tableau EASY
This blog discusses the implementation of tables in Tableau along with the process of performing operations on those tables.
Author Sohail Ali
1 upvote
Introduction to Context Filter EASY
In this article we are going to learn about Content Filter and their features and implementation in Tableau
Author Nitika
0 upvotes
Introduction to User Filter EASY
In this blog, we will discuss the introduction to user filters. We will discuss their features and learn about implementing these filters in Tableau.
Author kanishk11
0 upvotes
Making Word Cloud in Tableau
This article discusses creating word clouds in Tableau and visualising text data simply and attractively.
Author AkshatAggarwal
0 upvotes
Making Line Charts in Tableau
In this blog, we will discuss about making line charts in Tableau, their uses, advantages, disadvantages and how to make line charts in Tableau.
Author Aditya Gupta
0 upvotes
Data analytics in Python EASY
In this article, we'll explore how Python is used in data analytics, diving into numerical data analysis with NumPy and data manipulation with Pandas, accompanied by practical code examples.
Author Pallavi singh
0 upvotes
What are Bins in Tableau? MEDIUM
In this article, you will learn about bins in Tableau, the uses of bins, creating a bin in Tableau, and editing bins in Tableau.
Author Muskan Sharma
0 upvotes
What is the Level of Detail (LOD) in Tableau? EASY
This article answers the question, What is the Level of Detail (LOD) in Tableau? We will also cover features and limitations of it, many more.
Author Shiva
0 upvotes
Funnel Chart in Tableau
In this article, we will discuss about the Funnel Charts in Tableau, followed by their basic concepts, its uses, and its creation.
Author lakshayhere
0 upvotes
Introduction to Data Source Filter
In this article, we will discuss Data Source Filter, their features, and their implementation.
Author shruti_f97c
0 upvotes
Data Blending in Tableau EASY
In this blog, we will learn about data blending in Tableau. We will learn about Tableau and data blending and also discuss one example of data blending.
Author kanishk11
0 upvotes
Building Histograms in Tableau EASY
This article discusses histograms in Tableau, including their features, advantages, use cases, implementation, and some frequently asked questions.
Author Aayush Sharma
0 upvotes
Aggregating Data Using Tableau EASY
This article covers Aggregating Data Using Tableau, how to implement data aggregation, and rules for data aggregation and implementing it.
Author Shiva
0 upvotes
Hierarchies in Tableau
In this blog, we will be learning, how to create Hierarchies in Tableau with drill down and drill up respectively with proper examples.
Author Akanksha Burnwal
0 upvotes
Discrete Fields vs Continuous Fields in Tableau MEDIUM
In this article, you will learn briefly about discrete and continuous fields, their features, and the comparison between discrete fields and continuous fields in Tableau.
Author Muskan Sharma
0 upvotes
Joins in Tableau
In this blog, we will discuss joins in Tableau, their uses, types and how to create a join in Tableau.
Author Aditya Gupta
0 upvotes
Difference between Join and Blending in Tableau
This article will discuss the difference between them joins and blending in tableau, how to use and when to use those joins and Blends in data with examples.
Author Arya27
0 upvotes
Introduction to Dimension Filter
In this blog, we will discuss the introduction to dimension filter in detail, along with its implementation and features.
Author Akanksha Burnwal
0 upvotes
Building Maps in Tableau
In this blog, we will discuss the features and application of Building Maps in Tableau and discover its types and customization features.
Author Akanksha Burnwal
0 upvotes
Making Bar Charts in Tableau
Bar charts are used to compare data across categories, and in this blog, we will discuss the Bar charts in Tableau.
Author DEVENDRA KUMAR
1 upvote
Making Sparkline in Tableau EASY
This article covers the concept of sparkline in Tableau, its advantages and disadvantages, and how to make a sparkline.
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Data Wrangling Wrangling data from the internet and converting it into a usable form is one of the first steps in developing Machine Learning models. Learn more about various data wrangling techniques and tools.
Data Visualization One of the most important aspects of data analysis and extracting required solutions from a dataset through graphs, plots, and visuals is data visualization. It includes a number of libraries such as Matplotlib, Seaborn, HoloViews, and many others.
Data Preprocessing Clean and prepared data is a major factor in increasing the efficiency of any Machine Learning model. Data preprocessing allows one to convert any raw data into usable data while also filtering out unnecessary data or dimensions.
Outliers in Data Analysis How to handle and decipher data in datasets that are out of the expected range. Learn about outliers, their impact on any Machine Learning model, and how to detect and resolve them.

Deep Dive into Machine Learning

Learn about each technique or algorithm used in the Machine Learning domain for building models, solving real-world problems, and delving into one of the most exciting technological domains in the twenty-first century.
Supervised Learning Learn about Supervised Learning, one of the most widely used Machine Learning techniques. This technique requires trained data and input-output pairs, so are familiar with all aspects and master the technique.
Unsupervised Learning This is in contrast to Supervised Learning, which does not use trained data but has a long list of real-world applications and is widely used by many tech companies.
Reinforcement Learning Another machine learning technique that aids in achieving a better and more accurate output is reinforcement learning. This strategy improves a model's decision-making and decision-making path.
Important Differences Machine learning has many subcategories and techniques, each with some similarities and differences. It is critical to understand how these algorithms differ in order to make better decisions when dealing with classification or regression problems. One of the most significant distinctions is between supervised and unsupervised learning, technique, application, limitations, and data.
Applications of Machine Learning Discover how Machine Learning and its algorithms are used to solve real-world problems. Machine Learning has enabled everything from weather prediction to recommendation systems to self-driving cars.